Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark
Custom policy-learning pipelines in Spark fail for two coupled systems reasons: rowwise Python execution makes inference impractical, and driver-side candidate materialization makes split search fragile at feature scale. We present Spark Policy Toolkit, a semantics-governed systems toolkit for scalable policy learning in Spark. The toolkit provides two Spark-native primitives: partition-initialized vectorized inference through mapInPandas and mapInArrow, and collect-less split search that scores candidates on executors. Both primitives are governed by one fixed-input semantic contract: the same rows, feature order, treatment vocabulary, preprocessing manifest, and split boundaries must pr...
| Authors | Zeyu Bai |
|---|---|
| Primary category | cs.DC |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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